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Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information.

ABSTRACT: It is well believed that machine learning models could help to predict the formation energies of materials if all elemental and crystal structural details are known. In this paper, it is shown that even without detailed crystal structure information, the formation energies of binary compounds in various prototypes at the ground states can be reasonably evaluated using machine-learning feature abstraction to screen out the important features. By combining with the "white-box" sure independence screening and sparsifying operator (SISSO) approach, an interpretable and accurate formation energy model is constructed. The predicted formation energies of 183 experimental and 439 calculated stable binary compounds (E _hull = 0) are predicted using this model, and both show reasonable agreements with experimental and Materials Project's calculated values. The descriptor set is capable of reflecting the formation energies of binary compounds and is also consistent with the common understanding that the formation energy is mainly determined by electronegativity, electron affinity, bond energy, and other atomic properties. As crystal structure parameters are not necessary prerequisites, it can be widely applied to the formation energy prediction and classification of binary compounds in large quantities.

SUBMITTER: Mao Y

PROVIDER: S-EPMC8190927 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEvidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts.ResultsIn this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models.ConclusionsThis work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon ( http://crdd.osdd.net/raghava/rnacon).

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Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information.

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